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Matching events and activities by integrating behavioral aspects and label analysis
- Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. TheNowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.…
Author details: | Thomas Baier, Claudio Di CiccioORCiD, Jan MendlingORCiD, Mathias WeskeORCiDGND |
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DOI: | https://doi.org/10.1007/s10270-017-0603-z |
ISSN: | 1619-1366 |
ISSN: | 1619-1374 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/29706858 |
Title of parent work (English): | Software and systems modeling |
Publisher: | Springer |
Place of publishing: | Heidelberg |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/05/29 |
Publication year: | 2018 |
Release date: | 2021/12/07 |
Tag: | Business process intelligence; Constraint satisfaction; Declare; Event mapping; Natural language processing; Process mining |
Volume: | 17 |
Issue: | 2 |
Number of pages: | 26 |
First page: | 573 |
Last Page: | 598 |
Funding institution: | Vienna University of Economics and Business (WU) |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |